An alternative approach to the score-level fusion strategy is to jointly learn the horizontal and vertical view sequences, as done by the GLF-LSTM and SLF-LSTM cell architectures proposed in [1]} (introduced in Section II.B). Additionally, there are other alternative solutions, not originally designed for the same task, but which are flexible enough to be adopted for simultaneously learning from the horizontal and vertical view sequences.
Examples are the Spatio-Temporal LSTM (ST-LSTM) [2]} and the Dual-Sequence LSTM (DS-LSTM) cell architecture [3]}, which were originally designed to fuse different sequences for activity and speech recognition, respectively. These alternative LSTM cell architectures will be considered for benchmarking purposes, when comparing to the proposed solution in this paper.
